{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The cell below will get the data file, you only need to run it once "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(you do not need to do this if you have done it in the Interfacing_R notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2016-02-05 15:50:22--  ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/historical_data/former_toplevel/sequence.index\n",
      "           => 'sequence.index'\n",
      "Resolving ftp.1000genomes.ebi.ac.uk (ftp.1000genomes.ebi.ac.uk)... 193.62.192.8\n",
      "Connecting to ftp.1000genomes.ebi.ac.uk (ftp.1000genomes.ebi.ac.uk)|193.62.192.8|:21... connected.\n",
      "Logging in as anonymous ... Logged in!\n",
      "==> SYST ... done.    ==> PWD ... done.\n",
      "==> TYPE I ... done.  ==> CWD (1) /vol1/ftp/historical_data/former_toplevel ... done.\n",
      "==> SIZE sequence.index ... 67069489\n",
      "==> PASV ... done.    ==> RETR sequence.index ... done.\n",
      "Length: 67069489 (64M) (unauthoritative)\n",
      "\n",
      "sequence.index      100%[=====================>]  63.96M   419KB/s   in 2m 27s \n",
      "\n",
      "2016-02-05 15:52:54 (445 KB/s) - 'sequence.index' saved [67069489]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!rm sequence.index 2>/dev/null\n",
    "!wget -nd ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/historical_data/former_toplevel/sequence.index -O sequence.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tiago_antao/anaconda3/lib/python3.6/site-packages/rpy2/robjects/lib/ggplot2.py:67: UserWarning: This was designed againt ggplot2 version 2.2.1 but you have 3.0.0\n",
      "  warnings.warn('This was designed againt ggplot2 version %s but you have %s' % (TARGET_VERSION, ggplot2.__version__))\n"
     ]
    }
   ],
   "source": [
    "import rpy2.robjects as robjects\n",
    "import rpy2.robjects.lib.ggplot2 as ggplot2\n",
    "\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "seq.data <- read.delim('sequence.index', header=TRUE, stringsAsFactors=FALSE)\n",
    "seq.data$READ_COUNT <- as.integer(seq.data$READ_COUNT)\n",
    "seq.data$BASE_COUNT <- as.integer(seq.data$BASE_COUNT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tiago_antao/anaconda3/lib/python3.6/site-packages/rpy2/robjects/pandas2ri.py:191: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  res = PandasDataFrame.from_items(items)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "seq_data = %R seq.data\n",
    "print(type(seq_data))  #pandas dataframe!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "my_col = list(seq_data.columns).index(\"CENTER_NAME\")\n",
    "seq_data['CENTER_NAME'] = seq_data['CENTER_NAME'].apply(lambda x: x.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tiago_antao/anaconda3/lib/python3.6/site-packages/rpy2/robjects/pandas2ri.py:191: FutureWarning: from_items is deprecated. Please use DataFrame.from_dict(dict(items), ...) instead. DataFrame.from_dict(OrderedDict(items)) may be used to preserve the key order.\n",
      "  res = PandasDataFrame.from_items(items)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       " [1] \"FASTQ_FILE\"          \"MD5\"                 \"RUN_ID\"             \n",
       " [4] \"STUDY_ID\"            \"STUDY_NAME\"          \"CENTER_NAME\"        \n",
       " [7] \"SUBMISSION_ID\"       \"SUBMISSION_DATE\"     \"SAMPLE_ID\"          \n",
       "[10] \"SAMPLE_NAME\"         \"POPULATION\"          \"EXPERIMENT_ID\"      \n",
       "[13] \"INSTRUMENT_PLATFORM\" \"INSTRUMENT_MODEL\"    \"LIBRARY_NAME\"       \n",
       "[16] \"RUN_NAME\"            \"RUN_BLOCK_NAME\"      \"INSERT_SIZE\"        \n",
       "[19] \"LIBRARY_LAYOUT\"      \"PAIRED_FASTQ\"        \"WITHDRAWN\"          \n",
       "[22] \"WITHDRAWN_DATE\"      \"COMMENT\"             \"READ_COUNT\"         \n",
       "[25] \"BASE_COUNT\"          \"ANALYSIS_GROUP\"     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%R -i seq_data\n",
    "%R print(colnames(seq_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "seq_data <- seq_data[seq_data$WITHDRAWN==0, ]\n",
    "seq_data$POPULATION <- as.factor(seq_data$POPULATION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "bar <- ggplot(seq_data) +  aes(factor(CENTER_NAME)) + geom_bar() + theme(axis.text.x = element_text(angle = 90, hjust = 1))\n",
    "print(bar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "seq_data$POPULATION <- as.factor(seq_data$POPULATION)\n",
    "yri_ceu <- seq_data[seq_data$POPULATION %in% c(\"YRI\", \"CEU\") & seq_data$BASE_COUNT < 2E9 & seq_data$READ_COUNT < 3E7, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "scatter <- ggplot(yri_ceu, aes(x=BASE_COUNT, y=READ_COUNT, col=factor(ANALYSIS_GROUP), shape=POPULATION)) + geom_point()\n",
    "print(scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TableGrob (2 x 1) \"arrange\": 2 grobs\n",
       "  z     cells    name           grob\n",
       "1 1 (1-1,1-1) arrange gtable[layout]\n",
       "2 2 (2-2,1-1) arrange gtable[layout]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(gridExtra)\n",
    "library(grid)\n",
    "g <- grid.arrange(bar, scatter, ncol=1)\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "png \n",
       "  2 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "png('fig.png')\n",
    "g\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
